Startups, shipped to production.

AI for scaleups - when the platform decides whether you raise.

Most AI-native scaleups fail in the same place. The product demo is excellent. The series-A platform is over-fitted to the demo. By the time the team is preparing for series B, the system that won the seed round is the system slowing the company down - and the cost of rebuilding is competing with the cost of new features the market is now expecting. The decision in front of the CEO is whether to slow product velocity to fix the foundation, or to keep shipping on a stack the engineering leads no longer trust.

The right partner at this stage is not a body shop. It is a small group of senior engineers who have shipped at scale before, who can sit on your standups, push to your repo, take on-call, and make architectural calls that hold up at the next round. Junior engineers can write code; they cannot tell you which slice of the platform to invest in next, when to write the SOC 2 controls, when an external dependency becomes a single point of failure, and when an exciting new model is a distraction. That judgment is the product.

We ship this way deliberately. Senior, milestone-based, embedded - with audit-grade controls, signed traces, and observability from the first commit, not retrofitted before the diligence call. The handoff is just as deliberate: runbooks your team helped write, documentation that matches the code, and the on-call coverage during transition that lets the program survive the moment we leave. The deal we sign is that your team will be able to ship without us - and the test of the program is whether they actually do.

A scaleup AI platform, ready for the next round.

Built alongside MAIA and Sensei. Senior engineers, milestone-based, production-ready handoff.